{"ID":2880822,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.14042","arxiv_id":"2508.14042","title":"Sim-to-Real Dynamic Object Manipulation on Conveyor Systems via Optimization Path Shaping","abstract":"Realizing generalizable dynamic object manipulation on conveyor systems is important for enhancing manufacturing efficiency, as it eliminates specialized engineering for different scenarios. To this end, imitation learning emerges as a promising paradigm, leveraging expert demonstrations to teach a policy manipulation skills. Although the generalization of an imitation learning policy can be improved by increasing demonstrations, demonstration collection is labor-intensive. Besides, public dynamic object manipulation data is scarce. In this work, we address this data scarcity problem via generating demonstrations in a simulator. A significant challenge of using simulated data lies in the appearance gap between simulated and real-world observations. To tackle this challenge, we propose Geometry-Enhanced Model (GEM), which employs our designed appearance noise annealing strategy to shape the policy optimization path, thereby prioritizing the geometry information in observations. Extensive experiments in simulated and real-world tasks demonstrate that GEM can generalize across environment backgrounds, robot embodiments, motion dynamics, and object geometries. Notably, GEM is deployed in a real canteen for tableware collection. Without test-scene data, GEM achieves a success rate of over 97% across more than 10,000 operations.","short_abstract":"Realizing generalizable dynamic object manipulation on conveyor systems is important for enhancing manufacturing efficiency, as it eliminates specialized engineering for different scenarios. To this end, imitation learning emerges as a promising paradigm, leveraging expert demonstrations to teach a policy manipulation...","url_abs":"https://arxiv.org/abs/2508.14042","url_pdf":"https://arxiv.org/pdf/2508.14042v2","authors":"[\"Zhuoling Li\",\"Jinrong Yang\",\"Yong Zhao\",\"Liangliang Ren\",\"Xiaoyang Wu\",\"Zhenhua Xu\",\"Hengshuang Zhao\"]","published":"2025-08-19T17:59:59Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}